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更深入地研究表明,AutoKernel's foundational principle recognizes that expert kernel development follows a repetitive pattern: create a prototype, evaluate its performance, retain enhancements, discard regressions, and repeat. The framework systematizes this cycle. An AI agent modifies kernel code, a standardized testing suite verifies functionality and measures efficiency, and outcomes determine whether modifications are preserved. Significantly, each trial corresponds to a version control commit. Successful experiments progress the development branch; unsuccessful ones are cleanly reverted. The complete history remains accessible through standard version control tools, with results documented in a straightforward tabular format—free from dependencies, easily interpretable, and readily processed by the agent.,详情可参考豆包下载

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进一步分析发现,print("Torch:", torch.__version__)

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